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Machine Learning
Introduction
Module Overview (1:53)
Module SetUp Part1 (1:12)
Module SetUp Part2 (1:58)
H20 Model SetUp Part1 (8:46)
H20 Model SetUp Part2 (8:13)
Lime Resources (1:21)
Lime Documentation (5:26)
Introduction Summary (8:54)
Feature importance, each with different strengths
Predictions with LIME (6:02)
Creating an explainer with LIME (14:15)
Explainer with LIME Part2 (8:50)
Illustrating the significance of predictors with caret (16:18)
Depicting the weight of variables with xgboost (6:52)
Displaying the relevance of features with LIME (6:05)
Mapping out key predictors' influence (1:04)
Showcasing predictor impact (13:27)
randomForest (6:03)
Variable Importance Plots, focused on tidy, customizable plots (8:17)
Analyzing the Impact of Variable Adjustments and Testing Hypotheses
Identify the Research Problem & Formulate the Hypotheses (11:55)
Determine the Required Data & Collect Data with httr & rvest (9:50)
Clean & Organize the Data with dplyr, tidyr, &, data.table (1:58)
Statistical Tests & Method Selection with stats & MASS (7:26)
Variable Selection & Model Parameters with caret & glmnet (9:04)
Perform the Analysis with nnet, lme4, vegan, & nlme (16:18)
Interpret Findings & Check Assumptions with broom (6:38)
Confirm or Reject Hypotheses with multcomp & Car (12:25)
Reporting with rmarkdown (13:27)
Reporting with ggplot2 plotly (13:35)
Optimization at Various Thresholds (9:04)
Explaining The Optimization Results (14:29)
Sensitivity Analysis (10:20)
Adjusting Parameters to Retest the Assumptions (18:37)
Iterating with pmap() (6:13)
Developing a Recommendation Algorithm
Overview (6:13)
BSPF & SetUp (10:20)
Recipes for Discretization (8:39)
Creating A Recipe (1:58)
Binning with step_discretize() (13:27)
Dummy Variables & One Hot Encoding (9:04)
Bake() function (11:55)
Accessing the Binning Strategy Using tidy() (12:25)
Correlation & Visualization (16:18)
Data Manipulation Part1 (6:38)
Data Manipulation Part2 (9:50)
Data Manipulation Part3 (13:35)
Visualize Discretized Correlation with ggplot2 and Plotly (16:18)
Creating a Worksheet (5:31)
Filling Out the Worksheet Part1 (8:46)
Filling Out the Worksheet Part2 (1:21)
Filling Out the Worksheet Part3 (13:08)
Conclusion of the code and framework (4:28)
Machine Learning & Your Portafolio
Intro (9:33)
SetUp fluidPage, sidebarLayout, and mainPanel (6:05)
Server Logic with Shiny (4:28)
Debugging (8:50)
User Testing Part1 (1:21)
User Testing Part2 (8:54)
Choose a Platform (8:54)
Quantmod & Tidyquant (14:51)
PortfolioAnalytics (8:54)
ShinyWidgets (6:52)
Shinyjs (8:13)
DT Package (6:38)
Deployment and Production rsconnect (9:50)
Deployment and Production shinytest (7:26)
Deployment and Production packrat (1:58)
Shinymanager (6:38)
Shinymanager Part2 (8:17)
Database connection with pool (12:25)
Final Comments (11:55)
Perform the Analysis with nnet, lme4, vegan, & nlme
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